GEO (Generative Engine Optimization)

GEO (Generative Engine Optimization) is the practice of structuring and publishing content so that generative AI engines such as ChatGPT, Perplexity and Claude understand, retrieve and cite it in their answers. Where SEO optimizes for ranking in a list of links, GEO optimizes for being the cited source inside a generated answer. The term was formalized in the academic paper "GEO: Generative Engine Optimization" by Aggarwal et al., presented at ACM KDD 2024 (arXiv:2311.09735), which introduced GEO-bench, a benchmark of 10,000 queries across diverse domains.

The study found that content-level tactics can raise a source's visibility in generative answers by up to ~40%. The most effective levers were adding direct quotations (+27.8%), statistics (+25.9%) and cited sources (+24.9%) — while classic "keyword stuffing" was largely ineffective for generative engines. Effectiveness varies by domain, so the paper argues for topic-specific optimization rather than a single universal tactic. In practice, GEO combines technical access (allowing AI crawlers), structured data, answer-shaped content and machine-readable, open formats.